The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to an...The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.展开更多
Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the grid.This paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or n...Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the grid.This paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable time.The encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress exploitation.The performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the problem.Experimental results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.展开更多
A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system （SLHS）. An algorithm was proposed to select one （or a group of） sensor at each time from a set ...A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system （SLHS）. An algorithm was proposed to select one （or a group of） sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finallyo the algorithms are validated through an illustrative target tracking example.展开更多
To reduce resources consumption of parallel computation system, a static task scheduling opti- mization method based on hybrid genetic algorithm is proposed and validated, which can shorten the scheduling length of pa...To reduce resources consumption of parallel computation system, a static task scheduling opti- mization method based on hybrid genetic algorithm is proposed and validated, which can shorten the scheduling length of parallel tasks with precedence constraints. Firstly, the global optimal model and constraints are created to demonstrate the static task scheduling problem in heterogeneous distributed computing systems（HeDCSs）. Secondly, the genetic population is coded with matrix and used to search the total available time span of the processors, and then the simulated annealing algorithm is introduced to improve the convergence speed and overcome the problem of easily falling into local minimum point, which exists in the traditional genetic algorithm. Finally, compared to other existed scheduling algorithms such as dynamic level scheduling （ DLS）, heterogeneous earliest finish time （HEFr）, and longest dynamic critical path（ LDCP）, the proposed approach does not merely de- crease tasks schedule length, but also achieves the maximal resource utilization of parallel computa- tion system by extensive experiments.展开更多
The entransy theory, which can be used to optimize the heat transfer network of a solar power tower system （SPTS） and im- prove its energy efficiency, was introduced in this paper. Firstly, the irreversibility of th...The entransy theory, which can be used to optimize the heat transfer network of a solar power tower system （SPTS） and im- prove its energy efficiency, was introduced in this paper. Firstly, the irreversibility of the heat transfer processes in a SPTS was analyzed and the total entransy dissipation equation of a SPTS was derived. Then, two types of optimization problems （reduc- ing the total circulating flow rate or the total heat-exchanging area） of a SPTS were solved with conditional extremum model based on the formulas of total entransy dissipation. Finally, the entransy dissipation-based optimization principle was applied to a simple SPTS without re-heater and a complex SPTS with a re-heater. The results showed that under the chosen calculation conditions the minimum total thermal conductance was 19306.03 W K-~ for a SPTS without re-heater when the total heat ca- pacity rate of heat transfer fluid （HTF） was 3200 W K-1. The minimum total thermal conductance was about 7.9% lower than the value predicted based on the typical outlet temperature of a receiver. This meant that the total heat exchange area or initial investment could be effectively reduced under the prescribed total HTF circulating flow rate. We also studied the variation trends of the two optimized results including minimum total HTF heat capacity rate and minimum total thermal conductance. The minimum total HTF heat capacity rate decreased with the given total thermal conductance, the minimum total thermal conductance decreased first and then increased with the given total HTF heat capacity rate. We also found that for a SPTS with a re-heater, the mixing temperature and the mixing position of HTF had significant effects on the two types of optimization problems.展开更多
基金Supported by the National Basic Research Program of China ("973" Program)the National Natural Science Foundation of China (60872112, 10805012)+1 种基金the Natural Science Foundation of Zhejiang Province(Z207588)the College Science Research Project of Anhui Province (KJ2008B268)~~
文摘The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.
基金supported by the National Basic Research Program of China(No.2007CB316502)the National Natural Science Foundation of China(No.60534060)
文摘Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the grid.This paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable time.The encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress exploitation.The performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the problem.Experimental results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.
基金Foundation item： Project（2012AA051603） supported by the National High Technology Research and Development Program 863 Plan of China
文摘A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system （SLHS）. An algorithm was proposed to select one （or a group of） sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finallyo the algorithms are validated through an illustrative target tracking example.
基金Supported by the National Natural Science Foundation of China(No.61401496)
文摘To reduce resources consumption of parallel computation system, a static task scheduling opti- mization method based on hybrid genetic algorithm is proposed and validated, which can shorten the scheduling length of parallel tasks with precedence constraints. Firstly, the global optimal model and constraints are created to demonstrate the static task scheduling problem in heterogeneous distributed computing systems（HeDCSs）. Secondly, the genetic population is coded with matrix and used to search the total available time span of the processors, and then the simulated annealing algorithm is introduced to improve the convergence speed and overcome the problem of easily falling into local minimum point, which exists in the traditional genetic algorithm. Finally, compared to other existed scheduling algorithms such as dynamic level scheduling （ DLS）, heterogeneous earliest finish time （HEFr）, and longest dynamic critical path（ LDCP）, the proposed approach does not merely de- crease tasks schedule length, but also achieves the maximal resource utilization of parallel computa- tion system by extensive experiments.
基金supported by the National Natural Science Foundation of China(Grant No.U1261112)the Research Project of Chinese Ministry of Education(Grant Nos.113055A,20120201130006)
文摘The entransy theory, which can be used to optimize the heat transfer network of a solar power tower system （SPTS） and im- prove its energy efficiency, was introduced in this paper. Firstly, the irreversibility of the heat transfer processes in a SPTS was analyzed and the total entransy dissipation equation of a SPTS was derived. Then, two types of optimization problems （reduc- ing the total circulating flow rate or the total heat-exchanging area） of a SPTS were solved with conditional extremum model based on the formulas of total entransy dissipation. Finally, the entransy dissipation-based optimization principle was applied to a simple SPTS without re-heater and a complex SPTS with a re-heater. The results showed that under the chosen calculation conditions the minimum total thermal conductance was 19306.03 W K-~ for a SPTS without re-heater when the total heat ca- pacity rate of heat transfer fluid （HTF） was 3200 W K-1. The minimum total thermal conductance was about 7.9% lower than the value predicted based on the typical outlet temperature of a receiver. This meant that the total heat exchange area or initial investment could be effectively reduced under the prescribed total HTF circulating flow rate. We also studied the variation trends of the two optimized results including minimum total HTF heat capacity rate and minimum total thermal conductance. The minimum total HTF heat capacity rate decreased with the given total thermal conductance, the minimum total thermal conductance decreased first and then increased with the given total HTF heat capacity rate. We also found that for a SPTS with a re-heater, the mixing temperature and the mixing position of HTF had significant effects on the two types of optimization problems.
